Abstract A032: Interstitial insulin enrichment drives pancreatic cancer growth by enhancing cancer cell metabolic plasticity
Bibliographic record
Abstract
Abstract Metabolic diseases, such as type 2 diabetes, insulin resistance, and obesity, often coexist with pancreatic ductal adenocarcinoma (PDAC) and predict poor survival. Using a mouse model of diet-induced obesity, we showed that a high-fat diet (HFD) consumption that induces hyperinsulinemia, but not hyperglycemia, in mice accelerated the growth of xenografted patient-derived PDAC organoids (PDOs). Strikingly, even in the fasting state, insulin was highly enriched in the tumor interstitial fluid of pancreatic tumors under HFD. Modelling this elevated interstitial insulin concentration in vitro significantly increased PDO growth regardless of glucose levels. Mechanistically, chronic high-insulin exposure robustly enhances metabolic plasticity and nutrient utilization in PDOs by selectively upregulating distinct metabolic pathways in response to differences in extracellular glucose levels. Thus, the presence of robust insulin levels continuously supports tumor growth as glucose availability changes. Specifically, under low-glucose conditions, elevated insulin activates nutrient uptake machinery and amino acid metabolism, whereas it promotes aerobic glycolysis in high-glucose conditions. Pharmacological inhibition of these metabolic pathways attenuated insulin-mediated growth. Our study identifies insulin enrichment in the tumor-interstitial fluid as an important driver of metabolic adaptation in PDAC cells. Citation Format: Jeffrey SH. Lin, James D. Johnson, Christoph H. Borchers, David F. Schaeffer, Janel L. Kopp. Interstitial insulin enrichment drives pancreatic cancer growth by enhancing cancer cell metabolic plasticity [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_3):Abstract nr A032.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".